diff --git a/include/ceres/ceres.h b/include/ceres/ceres.h
index 61b8b94..7552a68 100644
--- a/include/ceres/ceres.h
+++ b/include/ceres/ceres.h
@@ -43,6 +43,8 @@
 #include "ceres/cost_function_to_functor.h"
 #include "ceres/covariance.h"
 #include "ceres/crs_matrix.h"
+#include "ceres/dynamic_autodiff_cost_function.h"
+#include "ceres/dynamic_numeric_diff_cost_function.h"
 #include "ceres/iteration_callback.h"
 #include "ceres/jet.h"
 #include "ceres/local_parameterization.h"
diff --git a/include/ceres/dynamic_autodiff_cost_function.h b/include/ceres/dynamic_autodiff_cost_function.h
index 5d8f188..f9342cd 100644
--- a/include/ceres/dynamic_autodiff_cost_function.h
+++ b/include/ceres/dynamic_autodiff_cost_function.h
@@ -1,5 +1,5 @@
 // Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2012 Google Inc. All rights reserved.
+// Copyright 2013 Google Inc. All rights reserved.
 // http://code.google.com/p/ceres-solver/
 //
 // Redistribution and use in source and binary forms, with or without
@@ -26,18 +26,17 @@
 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 // POSSIBILITY OF SUCH DAMAGE.
 //
-// Author: mierle@gmail.com (Keir Mierle)
-//         sameeragarwal@google.com (Sameer Agarwal)
-//         thadh@gmail.com (Thad Hughes)
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+//         mierle@gmail.com (Keir Mierle)
 //
 // This autodiff implementation differs from the one found in
-// autodiff_cost_function.h by supporting autodiff on cost functions with
-// variable numbers of parameters with variable sizes. With the other
-// implementation, all the sizes (both the number of parameter blocks and the
-// size of each block) must be fixed at compile time.
+// autodiff_cost_function.h by supporting autodiff on cost functions
+// with variable numbers of parameters with variable sizes. With the
+// other implementation, all the sizes (both the number of parameter
+// blocks and the size of each block) must be fixed at compile time.
 //
-// The functor API differs slightly from the API for fixed size autodiff; the
-// expected interface for the cost functors is:
+// The functor API differs slightly from the API for fixed size
+// autodiff; the expected interface for the cost functors is:
 //
 //   struct MyCostFunctor {
 //     template<typename T>
@@ -46,8 +45,9 @@
 //     }
 //   }
 //
-// Since the sizing of the parameters is done at runtime, you must also specify
-// the sizes after creating the dynamic autodiff cost function. For example:
+// Since the sizing of the parameters is done at runtime, you must
+// also specify the sizes after creating the dynamic autodiff cost
+// function. For example:
 //
 //   DynamicAutoDiffCostFunction<MyCostFunctor, 3> cost_function(
 //       new MyCostFunctor());
@@ -55,10 +55,11 @@
 //   cost_function.AddParameterBlock(10);
 //   cost_function.SetNumResiduals(21);
 //
-// Under the hood, the implementation evaluates the cost function multiple
-// times, computing a small set of the derivatives (four by default, controlled
-// by the Stride template parameter) with each pass. There is a tradeoff with
-// the size of the passes; you may want to experiment with the stride.
+// Under the hood, the implementation evaluates the cost function
+// multiple times, computing a small set of the derivatives (four by
+// default, controlled by the Stride template parameter) with each
+// pass. There is a tradeoff with the size of the passes; you may want
+// to experiment with the stride.
 
 #ifndef CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
 #define CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
diff --git a/include/ceres/dynamic_numeric_diff_cost_function.h b/include/ceres/dynamic_numeric_diff_cost_function.h
new file mode 100644
index 0000000..c30e0f1
--- /dev/null
+++ b/include/ceres/dynamic_numeric_diff_cost_function.h
@@ -0,0 +1,240 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: mierle@gmail.com (Keir Mierle)
+//         sameeragarwal@google.com (Sameer Agarwal)
+//         thadh@gmail.com (Thad Hughes)
+//
+// This numeric diff implementation differs from the one found in
+// numeric_diff_cost_function.h by supporting numericdiff on cost
+// functions with variable numbers of parameters with variable
+// sizes. With the other implementation, all the sizes (both the
+// number of parameter blocks and the size of each block) must be
+// fixed at compile time.
+//
+// The functor API differs slightly from the API for fixed size
+// numeric diff; the expected interface for the cost functors is:
+//
+//   struct MyCostFunctor {
+//     template<typename T>
+//     bool operator()(double const* const* parameters, double* residuals) const {
+//       // Use parameters[i] to access the i'th parameter block.
+//     }
+//   }
+//
+// Since the sizing of the parameters is done at runtime, you must
+// also specify the sizes after creating the
+// DynamicNumericDiffCostFunction. For example:
+//
+//   DynamicAutoDiffCostFunction<MyCostFunctor, CENTRAL> cost_function(
+//       new MyCostFunctor());
+//   cost_function.AddParameterBlock(5);
+//   cost_function.AddParameterBlock(10);
+//   cost_function.SetNumResiduals(21);
+
+#ifndef CERES_PUBLIC_DYNAMIC_NUMERIC_DIFF_COST_FUNCTION_H_
+#define CERES_PUBLIC_DYNAMIC_NUMERIC_DIFF_COST_FUNCTION_H_
+
+#include <cmath>
+#include <numeric>
+#include <vector>
+
+#include "ceres/cost_function.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/internal/eigen.h"
+#include "glog/logging.h"
+
+namespace ceres {
+
+template <typename CostFunctor, NumericDiffMethod method = CENTRAL>
+class DynamicNumericDiffCostFunction : public CostFunction {
+ public:
+  explicit DynamicNumericDiffCostFunction(CostFunctor* functor,
+                                          Ownership ownership = TAKE_OWNERSHIP,
+                                          double relative_step_size = 1e-6)
+      : functor_(functor),
+        ownership_(ownership),
+        relative_step_size_(relative_step_size) {
+  }
+
+  virtual ~DynamicNumericDiffCostFunction() {
+    if (ownership_ != TAKE_OWNERSHIP) {
+      functor_.release();
+    }
+  }
+
+  void AddParameterBlock(int size) {
+    mutable_parameter_block_sizes()->push_back(size);
+  }
+
+  void SetNumResiduals(int num_residuals) {
+    set_num_residuals(num_residuals);
+  }
+
+  virtual bool Evaluate(double const* const* parameters,
+                        double* residuals,
+                        double** jacobians) const {
+    CHECK_GT(num_residuals(), 0)
+        << "You must call DynamicNumericDiffCostFunction::SetNumResiduals() "
+        << "before DynamicNumericDiffCostFunction::Evaluate().";
+
+    const vector<int16>& block_sizes = parameter_block_sizes();
+    CHECK(!block_sizes.empty())
+        << "You must call DynamicNumericDiffCostFunction::AddParameterBlock() "
+        << "before DynamicNumericDiffCostFunction::Evaluate().";
+
+    bool status = (*functor_)(parameters, residuals);
+    if (jacobians == NULL) {
+      return status;
+    }
+
+    // Create local space for a copy of the parameters which will get mutated.
+    int parameters_size = accumulate(block_sizes.begin(), block_sizes.end(), 0);
+    vector<double> parameters_copy(parameters_size);
+    vector<double*> parameters_references_copy(block_sizes.size());
+    parameters_references_copy[0] = &parameters_copy[0];
+    for (int block = 1; block < block_sizes.size(); ++block) {
+      parameters_references_copy[block] = parameters_references_copy[block - 1]
+          + block_sizes[block - 1];
+    }
+
+    // Copy the parameters into the local temp space.
+    for (int block = 0; block < block_sizes.size(); ++block) {
+      memcpy(parameters_references_copy[block],
+             parameters[block],
+             block_sizes[block] * sizeof(*parameters[block]));
+    }
+
+    for (int block = 0; block < block_sizes.size(); ++block) {
+      if (jacobians[block] != NULL &&
+          !EvaluateJacobianForParameterBlock(block_sizes[block],
+                                             block,
+                                             relative_step_size_,
+                                             residuals,
+                                             &parameters_references_copy[0],
+                                             jacobians)) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+ private:
+  bool EvaluateJacobianForParameterBlock(const int parameter_block_size,
+                                         const int parameter_block,
+                                         const double relative_step_size,
+                                         double const* residuals_at_eval_point,
+                                         double** parameters,
+                                         double** jacobians) const {
+    using Eigen::Map;
+    using Eigen::Matrix;
+    using Eigen::Dynamic;
+    using Eigen::RowMajor;
+
+    typedef Matrix<double, Dynamic, 1> ResidualVector;
+    typedef Matrix<double, Dynamic, 1> ParameterVector;
+    typedef Matrix<double, Dynamic, Dynamic, RowMajor> JacobianMatrix;
+
+    int num_residuals = this->num_residuals();
+
+    Map<JacobianMatrix> parameter_jacobian(jacobians[parameter_block],
+                                           num_residuals,
+                                           parameter_block_size);
+
+    // Mutate one element at a time and then restore.
+    Map<ParameterVector> x_plus_delta(parameters[parameter_block],
+                                      parameter_block_size);
+    ParameterVector x(x_plus_delta);
+    ParameterVector step_size = x.array().abs() * relative_step_size;
+
+    // To handle cases where a paremeter is exactly zero, instead use
+    // the mean step_size for the other dimensions.
+    double fallback_step_size = step_size.sum() / step_size.rows();
+    if (fallback_step_size == 0.0) {
+      // If all the parameters are zero, there's no good answer. Use the given
+      // relative step_size as absolute step_size and hope for the best.
+      fallback_step_size = relative_step_size;
+    }
+
+    // For each parameter in the parameter block, use finite
+    // differences to compute the derivative for that parameter.
+    for (int j = 0; j < parameter_block_size; ++j) {
+      if (step_size(j) == 0.0) {
+        // The parameter is exactly zero, so compromise and use the
+        // mean step_size from the other parameters. This can break in
+        // many cases, but it's hard to pick a good number without
+        // problem specific knowledge.
+        step_size(j) = fallback_step_size;
+      }
+      x_plus_delta(j) = x(j) + step_size(j);
+
+      ResidualVector residuals(num_residuals);
+      if (!(*functor_)(parameters, &residuals[0])) {
+        // Something went wrong; bail.
+        return false;
+      }
+
+      // Compute this column of the jacobian in 3 steps:
+      // 1. Store residuals for the forward part.
+      // 2. Subtract residuals for the backward (or 0) part.
+      // 3. Divide out the run.
+      parameter_jacobian.col(j) = residuals;
+
+      double one_over_h = 1 / step_size(j);
+      if (method == CENTRAL) {
+        // Compute the function on the other side of x(j).
+        x_plus_delta(j) = x(j) - step_size(j);
+
+        if (!(*functor_)(parameters, &residuals[0])) {
+          // Something went wrong; bail.
+          return false;
+        }
+
+        parameter_jacobian.col(j) -= residuals;
+        one_over_h /= 2;
+      } else {
+        // Forward difference only; reuse existing residuals evaluation.
+        parameter_jacobian.col(j) -=
+            Map<const ResidualVector>(residuals_at_eval_point, num_residuals);
+      }
+      x_plus_delta(j) = x(j);  // Restore x_plus_delta.
+
+      // Divide out the run to get slope.
+      parameter_jacobian.col(j) *= one_over_h;
+    }
+    return true;
+  }
+
+  internal::scoped_ptr<CostFunctor> functor_;
+  Ownership ownership_;
+  const double relative_step_size_;
+};
+
+}  // namespace ceres
+
+#endif  // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
diff --git a/internal/ceres/CMakeLists.txt b/internal/ceres/CMakeLists.txt
index 610e816..7c23a68 100644
--- a/internal/ceres/CMakeLists.txt
+++ b/internal/ceres/CMakeLists.txt
@@ -241,6 +241,7 @@
   CERES_TEST(covariance)
   CERES_TEST(dense_sparse_matrix)
   CERES_TEST(dynamic_autodiff_cost_function)
+  CERES_TEST(dynamic_numeric_diff_cost_function)
   CERES_TEST(evaluator)
   CERES_TEST(gradient_checker)
   CERES_TEST(gradient_checking_cost_function)
diff --git a/internal/ceres/dynamic_numeric_diff_cost_function_test.cc b/internal/ceres/dynamic_numeric_diff_cost_function_test.cc
new file mode 100644
index 0000000..19f4d88
--- /dev/null
+++ b/internal/ceres/dynamic_numeric_diff_cost_function_test.cc
@@ -0,0 +1,519 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+//         mierle@gmail.com (Keir Mierle)
+
+#include <cstddef>
+
+#include "ceres/dynamic_numeric_diff_cost_function.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+const double kTolerance = 1e-6;
+
+// Takes 2 parameter blocks:
+//     parameters[0] is size 10.
+//     parameters[1] is size 5.
+// Emits 21 residuals:
+//     A: i - parameters[0][i], for i in [0,10)  -- this is 10 residuals
+//     B: parameters[0][i] - i, for i in [0,10)  -- this is another 10.
+//     C: sum(parameters[0][i]^2 - 8*parameters[0][i]) + sum(parameters[1][i])
+class MyCostFunctor {
+ public:
+  bool operator()(double const* const* parameters, double* residuals) const {
+    const double* params0 = parameters[0];
+    int r = 0;
+    for (int i = 0; i < 10; ++i) {
+      residuals[r++] = i - params0[i];
+      residuals[r++] = params0[i] - i;
+    }
+
+    double c_residual = 0.0;
+    for (int i = 0; i < 10; ++i) {
+      c_residual += pow(params0[i], 2) - 8.0 * params0[i];
+    }
+
+    const double* params1 = parameters[1];
+    for (int i = 0; i < 5; ++i) {
+      c_residual += params1[i];
+    }
+    residuals[r++] = c_residual;
+    return true;
+  }
+};
+
+TEST(DynamicNumericdiffCostFunctionTest, TestResiduals) {
+  vector<double> param_block_0(10, 0.0);
+  vector<double> param_block_1(5, 0.0);
+  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
+      new MyCostFunctor());
+  cost_function.AddParameterBlock(param_block_0.size());
+  cost_function.AddParameterBlock(param_block_1.size());
+  cost_function.SetNumResiduals(21);
+
+  // Test residual computation.
+  vector<double> residuals(21, -100000);
+  vector<double*> parameter_blocks(2);
+  parameter_blocks[0] = &param_block_0[0];
+  parameter_blocks[1] = &param_block_1[0];
+  EXPECT_TRUE(cost_function.Evaluate(&parameter_blocks[0],
+                                     residuals.data(),
+                                     NULL));
+  for (int r = 0; r < 10; ++r) {
+    EXPECT_EQ(1.0 * r, residuals.at(r * 2));
+    EXPECT_EQ(-1.0 * r, residuals.at(r * 2 + 1));
+  }
+  EXPECT_EQ(0, residuals.at(20));
+}
+
+
+TEST(DynamicNumericdiffCostFunctionTest, TestJacobian) {
+  // Test the residual counting.
+  vector<double> param_block_0(10, 0.0);
+  for (int i = 0; i < 10; ++i) {
+    param_block_0[i] = 2 * i;
+  }
+  vector<double> param_block_1(5, 0.0);
+  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
+      new MyCostFunctor());
+  cost_function.AddParameterBlock(param_block_0.size());
+  cost_function.AddParameterBlock(param_block_1.size());
+  cost_function.SetNumResiduals(21);
+
+  // Prepare the residuals.
+  vector<double> residuals(21, -100000);
+
+  // Prepare the parameters.
+  vector<double*> parameter_blocks(2);
+  parameter_blocks[0] = &param_block_0[0];
+  parameter_blocks[1] = &param_block_1[0];
+
+  // Prepare the jacobian.
+  vector<vector<double> > jacobian_vect(2);
+  jacobian_vect[0].resize(21 * 10, -100000);
+  jacobian_vect[1].resize(21 * 5, -100000);
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect[0].data());
+  jacobian.push_back(jacobian_vect[1].data());
+
+  // Test jacobian computation.
+  EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),
+                                     residuals.data(),
+                                     jacobian.data()));
+
+  for (int r = 0; r < 10; ++r) {
+    EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
+    EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
+  }
+  EXPECT_EQ(420, residuals.at(20));
+  for (int p = 0; p < 10; ++p) {
+    // Check "A" Jacobian.
+    EXPECT_NEAR(-1.0, jacobian_vect[0][2*p * 10 + p], kTolerance);
+    // Check "B" Jacobian.
+    EXPECT_NEAR(+1.0, jacobian_vect[0][(2*p+1) * 10 + p], kTolerance);
+    jacobian_vect[0][2*p * 10 + p] = 0.0;
+    jacobian_vect[0][(2*p+1) * 10 + p] = 0.0;
+  }
+
+  // Check "C" Jacobian for first parameter block.
+  for (int p = 0; p < 10; ++p) {
+    EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);
+    jacobian_vect[0][20 * 10 + p] = 0.0;
+  }
+  for (int i = 0; i < jacobian_vect[0].size(); ++i) {
+    EXPECT_NEAR(0.0, jacobian_vect[0][i], kTolerance);
+  }
+
+  // Check "C" Jacobian for second parameter block.
+  for (int p = 0; p < 5; ++p) {
+    EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);
+    jacobian_vect[1][20 * 5 + p] = 0.0;
+  }
+  for (int i = 0; i < jacobian_vect[1].size(); ++i) {
+    EXPECT_NEAR(0.0, jacobian_vect[1][i], kTolerance);
+  }
+}
+
+TEST(DynamicNumericdiffCostFunctionTest, JacobianWithFirstParameterBlockConstant) {  // NOLINT
+  // Test the residual counting.
+  vector<double> param_block_0(10, 0.0);
+  for (int i = 0; i < 10; ++i) {
+    param_block_0[i] = 2 * i;
+  }
+  vector<double> param_block_1(5, 0.0);
+  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
+      new MyCostFunctor());
+  cost_function.AddParameterBlock(param_block_0.size());
+  cost_function.AddParameterBlock(param_block_1.size());
+  cost_function.SetNumResiduals(21);
+
+  // Prepare the residuals.
+  vector<double> residuals(21, -100000);
+
+  // Prepare the parameters.
+  vector<double*> parameter_blocks(2);
+  parameter_blocks[0] = &param_block_0[0];
+  parameter_blocks[1] = &param_block_1[0];
+
+  // Prepare the jacobian.
+  vector<vector<double> > jacobian_vect(2);
+  jacobian_vect[0].resize(21 * 10, -100000);
+  jacobian_vect[1].resize(21 * 5, -100000);
+  vector<double*> jacobian;
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect[1].data());
+
+  // Test jacobian computation.
+  EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),
+                                     residuals.data(),
+                                     jacobian.data()));
+
+  for (int r = 0; r < 10; ++r) {
+    EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
+    EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
+  }
+  EXPECT_EQ(420, residuals.at(20));
+
+  // Check "C" Jacobian for second parameter block.
+  for (int p = 0; p < 5; ++p) {
+    EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);
+    jacobian_vect[1][20 * 5 + p] = 0.0;
+  }
+  for (int i = 0; i < jacobian_vect[1].size(); ++i) {
+    EXPECT_EQ(0.0, jacobian_vect[1][i]);
+  }
+}
+
+TEST(DynamicNumericdiffCostFunctionTest, JacobianWithSecondParameterBlockConstant) {  // NOLINT
+  // Test the residual counting.
+  vector<double> param_block_0(10, 0.0);
+  for (int i = 0; i < 10; ++i) {
+    param_block_0[i] = 2 * i;
+  }
+  vector<double> param_block_1(5, 0.0);
+  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
+      new MyCostFunctor());
+  cost_function.AddParameterBlock(param_block_0.size());
+  cost_function.AddParameterBlock(param_block_1.size());
+  cost_function.SetNumResiduals(21);
+
+  // Prepare the residuals.
+  vector<double> residuals(21, -100000);
+
+  // Prepare the parameters.
+  vector<double*> parameter_blocks(2);
+  parameter_blocks[0] = &param_block_0[0];
+  parameter_blocks[1] = &param_block_1[0];
+
+  // Prepare the jacobian.
+  vector<vector<double> > jacobian_vect(2);
+  jacobian_vect[0].resize(21 * 10, -100000);
+  jacobian_vect[1].resize(21 * 5, -100000);
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect[0].data());
+  jacobian.push_back(NULL);
+
+  // Test jacobian computation.
+  EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),
+                                     residuals.data(),
+                                     jacobian.data()));
+
+  for (int r = 0; r < 10; ++r) {
+    EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
+    EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
+  }
+  EXPECT_EQ(420, residuals.at(20));
+  for (int p = 0; p < 10; ++p) {
+    // Check "A" Jacobian.
+    EXPECT_NEAR(-1.0, jacobian_vect[0][2*p * 10 + p], kTolerance);
+    // Check "B" Jacobian.
+    EXPECT_NEAR(+1.0, jacobian_vect[0][(2*p+1) * 10 + p], kTolerance);
+    jacobian_vect[0][2*p * 10 + p] = 0.0;
+    jacobian_vect[0][(2*p+1) * 10 + p] = 0.0;
+  }
+
+  // Check "C" Jacobian for first parameter block.
+  for (int p = 0; p < 10; ++p) {
+    EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);
+    jacobian_vect[0][20 * 10 + p] = 0.0;
+  }
+  for (int i = 0; i < jacobian_vect[0].size(); ++i) {
+    EXPECT_EQ(0.0, jacobian_vect[0][i]);
+  }
+}
+
+// Takes 3 parameter blocks:
+//     parameters[0] (x) is size 1.
+//     parameters[1] (y) is size 2.
+//     parameters[2] (z) is size 3.
+// Emits 7 residuals:
+//     A: x[0] (= sum_x)
+//     B: y[0] + 2.0 * y[1] (= sum_y)
+//     C: z[0] + 3.0 * z[1] + 6.0 * z[2] (= sum_z)
+//     D: sum_x * sum_y
+//     E: sum_y * sum_z
+//     F: sum_x * sum_z
+//     G: sum_x * sum_y * sum_z
+class MyThreeParameterCostFunctor {
+ public:
+  template <typename T>
+  bool operator()(T const* const* parameters, T* residuals) const {
+    const T* x = parameters[0];
+    const T* y = parameters[1];
+    const T* z = parameters[2];
+
+    T sum_x = x[0];
+    T sum_y = y[0] + 2.0 * y[1];
+    T sum_z = z[0] + 3.0 * z[1] + 6.0 * z[2];
+
+    residuals[0] = sum_x;
+    residuals[1] = sum_y;
+    residuals[2] = sum_z;
+    residuals[3] = sum_x * sum_y;
+    residuals[4] = sum_y * sum_z;
+    residuals[5] = sum_x * sum_z;
+    residuals[6] = sum_x * sum_y * sum_z;
+    return true;
+  }
+};
+
+class ThreeParameterCostFunctorTest : public ::testing::Test {
+ protected:
+  virtual void SetUp() {
+    // Prepare the parameters.
+    x_.resize(1);
+    x_[0] = 0.0;
+
+    y_.resize(2);
+    y_[0] = 1.0;
+    y_[1] = 3.0;
+
+    z_.resize(3);
+    z_[0] = 2.0;
+    z_[1] = 4.0;
+    z_[2] = 6.0;
+
+    parameter_blocks_.resize(3);
+    parameter_blocks_[0] = &x_[0];
+    parameter_blocks_[1] = &y_[0];
+    parameter_blocks_[2] = &z_[0];
+
+    // Prepare the cost function.
+    typedef DynamicNumericDiffCostFunction<MyThreeParameterCostFunctor>
+      DynamicMyThreeParameterCostFunction;
+    DynamicMyThreeParameterCostFunction * cost_function =
+      new DynamicMyThreeParameterCostFunction(
+        new MyThreeParameterCostFunctor());
+    cost_function->AddParameterBlock(1);
+    cost_function->AddParameterBlock(2);
+    cost_function->AddParameterBlock(3);
+    cost_function->SetNumResiduals(7);
+
+    cost_function_.reset(cost_function);
+
+    // Setup jacobian data.
+    jacobian_vect_.resize(3);
+    jacobian_vect_[0].resize(7 * x_.size(), -100000);
+    jacobian_vect_[1].resize(7 * y_.size(), -100000);
+    jacobian_vect_[2].resize(7 * z_.size(), -100000);
+
+    // Prepare the expected residuals.
+    const double sum_x = x_[0];
+    const double sum_y = y_[0] + 2.0 * y_[1];
+    const double sum_z = z_[0] + 3.0 * z_[1] + 6.0 * z_[2];
+
+    expected_residuals_.resize(7);
+    expected_residuals_[0] = sum_x;
+    expected_residuals_[1] = sum_y;
+    expected_residuals_[2] = sum_z;
+    expected_residuals_[3] = sum_x * sum_y;
+    expected_residuals_[4] = sum_y * sum_z;
+    expected_residuals_[5] = sum_x * sum_z;
+    expected_residuals_[6] = sum_x * sum_y * sum_z;
+
+    // Prepare the expected jacobian entries.
+    expected_jacobian_x_.resize(7);
+    expected_jacobian_x_[0] = 1.0;
+    expected_jacobian_x_[1] = 0.0;
+    expected_jacobian_x_[2] = 0.0;
+    expected_jacobian_x_[3] = sum_y;
+    expected_jacobian_x_[4] = 0.0;
+    expected_jacobian_x_[5] = sum_z;
+    expected_jacobian_x_[6] = sum_y * sum_z;
+
+    expected_jacobian_y_.resize(14);
+    expected_jacobian_y_[0] = 0.0;
+    expected_jacobian_y_[1] = 0.0;
+    expected_jacobian_y_[2] = 1.0;
+    expected_jacobian_y_[3] = 2.0;
+    expected_jacobian_y_[4] = 0.0;
+    expected_jacobian_y_[5] = 0.0;
+    expected_jacobian_y_[6] = sum_x;
+    expected_jacobian_y_[7] = 2.0 * sum_x;
+    expected_jacobian_y_[8] = sum_z;
+    expected_jacobian_y_[9] = 2.0 * sum_z;
+    expected_jacobian_y_[10] = 0.0;
+    expected_jacobian_y_[11] = 0.0;
+    expected_jacobian_y_[12] = sum_x * sum_z;
+    expected_jacobian_y_[13] = 2.0 * sum_x * sum_z;
+
+    expected_jacobian_z_.resize(21);
+    expected_jacobian_z_[0] = 0.0;
+    expected_jacobian_z_[1] = 0.0;
+    expected_jacobian_z_[2] = 0.0;
+    expected_jacobian_z_[3] = 0.0;
+    expected_jacobian_z_[4] = 0.0;
+    expected_jacobian_z_[5] = 0.0;
+    expected_jacobian_z_[6] = 1.0;
+    expected_jacobian_z_[7] = 3.0;
+    expected_jacobian_z_[8] = 6.0;
+    expected_jacobian_z_[9] = 0.0;
+    expected_jacobian_z_[10] = 0.0;
+    expected_jacobian_z_[11] = 0.0;
+    expected_jacobian_z_[12] = sum_y;
+    expected_jacobian_z_[13] = 3.0 * sum_y;
+    expected_jacobian_z_[14] = 6.0 * sum_y;
+    expected_jacobian_z_[15] = sum_x;
+    expected_jacobian_z_[16] = 3.0 * sum_x;
+    expected_jacobian_z_[17] = 6.0 * sum_x;
+    expected_jacobian_z_[18] = sum_x * sum_y;
+    expected_jacobian_z_[19] = 3.0 * sum_x * sum_y;
+    expected_jacobian_z_[20] = 6.0 * sum_x * sum_y;
+  }
+
+ protected:
+  vector<double> x_;
+  vector<double> y_;
+  vector<double> z_;
+
+  vector<double*> parameter_blocks_;
+
+  scoped_ptr<CostFunction> cost_function_;
+
+  vector<vector<double> > jacobian_vect_;
+
+  vector<double> expected_residuals_;
+
+  vector<double> expected_jacobian_x_;
+  vector<double> expected_jacobian_y_;
+  vector<double> expected_jacobian_z_;
+};
+
+TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterResiduals) {
+  vector<double> residuals(7, -100000);
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       NULL));
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+}
+
+TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterJacobian) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect_[0].data());
+  jacobian.push_back(jacobian_vect_[1].data());
+  jacobian.push_back(jacobian_vect_[2].data());
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);
+  }
+
+  for (int i = 0; i < 14; ++i) {
+    EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);
+  }
+
+  for (int i = 0; i < 21; ++i) {
+    EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);
+  }
+}
+
+TEST_F(ThreeParameterCostFunctorTest,
+       ThreeParameterJacobianWithFirstAndLastParameterBlockConstant) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect_[1].data());
+  jacobian.push_back(NULL);
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 14; ++i) {
+    EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);
+  }
+}
+
+TEST_F(ThreeParameterCostFunctorTest,
+       ThreeParameterJacobianWithSecondParameterBlockConstant) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect_[0].data());
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect_[2].data());
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);
+  }
+
+  for (int i = 0; i < 21; ++i) {
+    EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);
+  }
+}
+
+}  // namespace internal
+}  // namespace ceres